Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

Deep Residual Learning for Image Recognition

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Adam: A Method for Stochastic Optimization

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

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Identity mappings in deep residual networks

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Large-Scale Machine Learning with Stochastic Gradient Descent

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United Nations Environment Programme

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Traffic Flow Prediction With Big Data: A Deep Learning Approach

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Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

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A review on the prediction of building energy consumption

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Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

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Towards Sustainable Mosques: A Review of AI and ML Approaches for Energy Use Forecasting

Larhlida, A., Mana, A., Fathi, A. S., Ouhammou, B., Sadoune, Z., & Jamil, A. (2025) · Designs · doi:10.3390/designs9060124

Multi-Objective Optimisation for National Residential Retrofits: Scalable Bottom-Up Impact AssessmentsPreprint

Oliver, S., Seyedzadeh, S., & Rahimian, F. P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6222322

Unraveling drivers of urban heat in cold regions: Seasonal perspective on LST and governing factors using SHAP analysisPreprint

Han, D., Wang, M., & Xiang, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6237966

Community-led data collection approach leading to energy labeling and retrofit decision framework with case application

Debnath, S., Mohammed, N. A. C., Adesanya, M. A., Halliday, A., Farooque, A. A., & Singh, K. (2026) · Energy and Built Environment · doi:10.1016/j.enbenv.2026.02.009

A centroid time indicator for quantitative assessment of duck curve impactsPreprint

Jeon, S., & Kim, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6335796

Vers une approche intégrée d’aide à la planification énergétique territoriale : application à la rénovation énergétique des bâtiments

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Identification and simulation of physical systems with structured deep learning and inductive knowledge

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AI-Enhanced Urban Building Energy Modeling for Health-Driven Decarbonization in Vulnerable Communities

Abbasabadi, N., Moroseos, T., Ashayeri, M., & Meek, C. (2026) · Architecture · doi:10.3390/architecture6020084

Trustworthy Data-Driven Hybrid Modeling of Building Energy Performance and Greenhouse Gas Emissions

GÜNGÖR, A., Nur, A., Rüstemli, S., KÜRKER, F., Şahin, G., Akin, E., Adewole, K. S., & Jacobsson, A. (2026) · Buildings · doi:10.3390/buildings16112260

Experimental comparison of mechanical properties of PTFE-based composites reinforced with graphite and glass fibers

Hashemi, S. J., Aqdasi, A., Sadooghi, A., Mirzamohammadi, S., Rahmani, F., & Jafari, S. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08941-2

How much physics is enough? A minimum-input hybrid model with uncalibrated EnergyPlus for out-of-distribution set point prediction: a case study of temperature setbacks in a multi-zone campus buildingPreprint

Ho, C. O., & Jain, R. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7151174

CUBE: An open-source calculator for urban building energy enabling city-scale modeling and batch simulation

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An AI-augmented urban building energy Modeling framework integrating physics-based simulation, machine learning, and computer vision: The CityEnerTwin UBEM module

Abbasabadi, N., Worthy, A., Zhang, E. Z., Zhao, Y., & Liu, Y. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117984

Building Energy Consumption Prediction Integrating Stereo Photogrammetry and GIS-Based Urban Digital Twins with Machine Learning

Güntel, A., Aydınoğlu, A. Ç., & Şişman, S. (2026) · Land · doi:10.3390/land15091589

A Dynamic Shadow-Aware Graph Neural Network for Accurate Energy Prediction in Building ClustersPreprint

Xu, C., Cheng, R., Li, G., & Sun, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7450636

SBEM at scale: Building-specific retrofit analytics for 2.4 million Massachusetts residential buildings via surrogate-accelerated stochastic semantic building energy modelingPreprint

Wolk, S., Guettler, D., Cheung, E., & Reinhart, C. F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7450635

Advancing energy accessibility, equity, and sustainability with city information model for renewable, storage and infrastructure

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Decoupling visual semantics via explicit representation learning for urban building energy modeling

Shen, Y., Wang, X., Zhao, D., Chen, Z., Huang, C., Hu, X., Zi, J., Gong, L., Sun, Y., & Yao, J. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105335

DELFT-UBEM: An automated database-driven pipeline for district-scale building energy modeling, demonstrated on Dutch district retrofit

Jalilzadeh, A., Rafiee, A., Konstantinou, T., de Graaf, S., Scholten, H. J., & van Oosterom, P. J. M. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118345

Machine learning for climate-responsive urban design and planning: a scoping review

Manapragada, N. V. S. K., Vartholomaios, A., & Natanian, J. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107991

Navigating the evolving strategies and tools for building energy efficiency

Atta, I., Bakhoum, E. S., & Marzouk, M. M. (2026) · Energy Efficiency · doi:10.1007/s12053-026-10477-8

Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow

Nutkiewicz, A., Zheng, Y., & Jain, R. K. (2017) · Energy Procedia · doi:10.1016/j.egypro.2017.12.614 · Cited by 27

Exploring the Integration of Simulation and Deep Learning Models for Urban Building Energy Modeling and Retrofit Analysis

Nutkiewicz, A., & Jain, R. K. (2020) · Building Simulation Conference proceedings · doi:10.26868/25222708.2019.210264 · Cited by 8

A scalable and efficient framework for city-scale building energy modeling with microclimate considerations

Liu, K., Xu, X., Lin, D., Zhang, R., Zhao, L., Abuduwayiti, A., & Causone, F. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107187 · Cited by 6

A framework for augmenting simulation-based building energy models with earth-observational microclimate data products using machine learning predictionsPreprint

Worthy, A., Ashayeri, M., Marshall, J. D., & Abbasabadi, N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5554130

Construction and Research of a Data-Driven Energy Consumption Evaluation Model for Urban Building Operation

Gao, G., & Yang, S. (2023) · IEEE Access · doi:10.1109/access.2023.3340431 · Cited by 8